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Validating hidden Markov models for seabird behavioural inference.

Rebecca A Akeresola1,2, Adam Butler2, Esther L Jones2

  • 1School of Mathematics and Maxwell Institute for Mathematical Sciences University of Edinburgh Edinburgh UK.

Ecology and Evolution
|March 5, 2024
PubMed
Summary
This summary is machine-generated.

Hidden Markov models (HMMs) accurately infer seabird behaviors from tracking data, validated by visual tracking. This improves conservation planning by providing reliable animal movement insights.

Keywords:
GPS dataconservationmovement datamovement modellingvisual tracking

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Area of Science:

  • Marine ecology
  • Animal behavior
  • Conservation science

Background:

  • Observing animal behavior in marine environments is challenging.
  • Hidden Markov models (HMMs) infer behaviors from telemetry data.
  • Validating these inferred behaviors is difficult due to lack of ground truth data.

Purpose of the Study:

  • To investigate the accuracy of HMMs for inferring seabird behaviors.
  • To validate HMM-derived behaviors using simultaneous visual tracking data.
  • To assess the conservation implications of accurate behavioral inference.

Main Methods:

  • Utilized a unique dataset of simultaneous boat-based visual tracking and seabird behavior observations.
  • Applied Hidden Markov Models (HMMs) to telemetry data to infer animal states.
  • Compared HMM-inferred behaviors against a 'gold standard' of manually classified visual tracking data.

Main Results:

  • HMM accuracy ranged from 71% to 87% during chick-rearing and 54% to 70% during incubation.
  • Model choice had minimal impact on accuracy, even with varying AIC values.
  • Missed foraging bouts, critical for conservation, were identified as lasting only a few seconds.

Conclusions:

  • HMMs reliably identify key conservation-relevant seabird behaviors when validated.
  • Visual tracking data serves as a robust method for validating HMM behavioral inferences.
  • Increased confidence in using HMMs for animal behavior analysis in conservation is warranted, with a call for integrated validation data collection.